Transcript The Next Era in Immuno-Oncology

The Next Era in Immuno-Oncology
Dr. George Poste
Chief Scientist, Complex Adaptive Systems Initiative
and Del E. Webb Chair in Health Innovation
Arizona State University
[email protected]
www.casi.asu.edu
Presentation at Community Oncology Alliance Annual Meeting
Orlando, FL
April 15, 2016
Declared Interests
Board of Directors
-Caris Life Sciences
- Monsanto
- Exelixis
- Bulletin Atomic
Scientists
- Life Sciences
Foundation
Scientific Advisory Boards
- Synthetic Genomics
- Human Longevity Inc.
- University of
Michigan, Alfred A.
Taubman Medical
Research Institute
Advisory/Consultancy
˗
˗
USG: Depts. of
Defense and
Homeland
Security
US Academy of
Medicine Global
Forum on Health
Slides available @ http://casi.asu.edu/
Confronting the Clinical, Economic and Human Toll of Cancer
US Cancer Deaths (2014)
580,000
US Cancer Prevalence Estimates 2010 and 2020
# People (thousands)
%
Site
2010
2020
change
Breast
3461
4538
31
Prostate
2311
3265
41
Colorectal
1216
1517
25
Melanoma
1225
1714
40
Lymphoma
639
812
27
Uterus
588
672
15
Bladder
514
629
22
Lung
374
457
22
Kidney
308
426
38
Leukemia
263
240
29
All Sites
13,772
18,071
32
From: A.B. Mariotto et al. (2011) J. Nat. Cancer Inst. 103, 117
Cancer as a Complex Adaptive System:
The Dynamic Interaction Between Host Immune Defenses
and Relentless Emergence of Phenotypically Diverse Tumor Cell Clones
Escape From Controls
for Normal
Tissue Architecture
Genome Instability and
Emergence of
Clonal Variants
Use of Host
Systems to
Promote Progression
Invasion
and
Metastasis
Evasion of
Clonal Detection/Destruction
by Host Immune System
Emergence
of Drug-Resistant
Clones
Pembrolizumab and
Therapy of Metastatic Melanoma
in President J. Carter
Saturation TV Advertising
Cancer Immunotherapy Investment by Big Pharma:
Big Bucks, Big Risks, Big Payoffs?
The Rationale for Cancer Immunotherapy
Overcoming the Tumor Cell Heterogeneity Problem?
Circumventing the Omnipresent Resistance Problem
in Chemotherapy and Targeted Therapies?
Cancer as a Complex Adaptive System
The Relentless Emergence of Phenotypically Diverse
Tumor Clones and Subclones During Progression
Rx Resistance
• intrinsic
• acquired
The Extravagant Landscape of
Inter-individual Genomic Alterations in Cancer
(Cell 2012: 150, 1107 and 1121)
Mutations in Individual
Non-small Cell Lung Cancers
Drug Targets in Individual
Non-Small Cell Lung Cancers
 “malignant snowflakes”: each cancer carries multiple
unique mutations and other genome perturbations
 disturbing implications for therapeutic ‘cure’ and
development of new Rx
The Multi-Dimensional Matrix for Cancer Immunotherapy
cellular and humoral
multi-component system
and complex regulatory networks
host
immune
system
tumor cell (epi) genetic
and
phenotypic heterogeneity
and
clonal diversification
tumor
tumor
microenvironments
dynamic tumor-host
cell interactions and
complex immune
activation/suppression
pathways
The Multi-Dimensional Matrix for Cancer Immunotherapy
cellular and humoral
multi-component system
and complex regulatory networks
host
immune
system
tumor cell (epi) genetic
and
phenotypic heterogeneity
and
clonal diversification
tumor
tumor
microenvironments
impact of therapy
• emergence of resistance
• immune functions
dynamic tumor-host
cell interactions and
complex immune
activation/suppression
pathways
Anti-Cancer Immunotherapies
 passive therapies
 active therapies
 combination therapies
Passive Immunotherapy:
Enhancement of Anti-Tumor Activities Without Direct
Modification of Intrinsic Host Immune Functions
 therapeutic anti-tumor antibodies
 adoptive transfer of cytotoxic T lymphocytes
(TILS, TCRs, CARs)
 oncolytic viruses
Passive Immunotherapy With Antibodies
Fineartamerica.com
FDA-Approved Immunotherapy Agents
monoclonal antibodies (mabs)
MOA
Agent
Year
Indication
CD52
Alemtuzumab
2001
CLL
CD20
Ofatumumab
2009
CLL
CD20
Rituximab
1997
NHL
2010
CLL
CD38
Daratumumab
2015
Multiple Myeloma
HER2
Trastuzumab
1998
Breast cancer
2010
Gastric cancer
2004
Colorectal cancer
2011
Head/neck cancer
EGF
Cetuximab
CD20 ADC
Y-Ibritumomab
tiutexan
2002
NHL
CD30 ADC
brentuximab vedotin
2011
Hodgkin lymphoma, ALCL
BITE antibody constructs: Bi- and Multi-Specific Antibodies
MOA
Agent
Year
Indication
CD3/CD19
Blinatumomab
2014
ALL
Intrinsic Limitations of Passive Antibody Therapies
Tumor Cell Antigenic Heterogeneity and Dynamic
Emergence of New Antigenically Different Clones
Active Immunotherapies
Clone Wars
Relentless Emergence of New Tumor Cell Clones
During Tumor Progression and Immune Evasion
versus
Activation of Host T Lymphocyte Clones to
Kill (Neo)Antigen-Specific Tumor Clones
The Promise of Immunotherapy:
Circumventing the Inevitable Drug Resistance Problem in Targeted Rx Therapy
versus Restoration of Effective Immune Surveillance
Cytotoxic
clones / tumor
T cells
neoantigens
clones
targeted
drugs
Rx1
NA1
Rx2
NA2
Rx3
NA3
Rx4
NA4
Rx5
NA5
Rx-resistant
clones/
Rx refractory
disease
NAn1
NAn2
immuotherapeutic
regimens
adaptive
evolution
of immune
response
and expanded
cytotoxic
T cell
responses
Mapping the Molecular Control Pathways in Immune
Responses for Rational Design of New Immunotherapeutics
Understanding Molecular Signaling (Information)
Systems and Feedback Control in the Immune System
The Immunostat:
The Constantly Shifting Balance Between Activation and Suppression
Active Immunotherapies
activation of cytotoxic T cells
 immunostimulatory cytokines
 vaccine-induced expansion of cytotoxic T cells to
cancer neoantigens
 unanticipated immune-stimulation by targeted
Rx/SOC
blockade/inhibition of immunosuppressive pathways
 immune checkpoint inhibitors (CTLA-4, PD-1, PD-L1)
 inhibition of Tregs and myeloid-derived suppression
cells
 inhibition of immunosuppressive signals from non-
immune cells in the tumor microenvironment
Cancer Immunotherapy
C.L. Batlevi; et. al. (2016) Nature Reviews │ Clinical Oncology 13,25
The Immune-Checkpoint Axis
 complex networks of multiple negative checkpoint
regulators to limit the scale and duration of
activated immune reactions
 maintain self-tolerance
 prevent autoimmunity
 limit cytokine release storms
Immune Checkpoint Inhibitors
Timelines of FDA Approvals
March 2011
-
ipilimumab: melanoma
September 2014
-
pembrolizumab : melanoma
October 2014
- pembrolizumab: NSCLC
December 2014
- nivolumab: melanoma
March 2015
- nivolumab: NSCLC
October 2015
- nivolumab: renal cancer
Combination Immunotherapies
Combination Immunotherapy
 ipilimumab + nivolumab
- melanoma 60% response versus single agent
responses 44% (nivo), 19% (ipi)
- 12% CR
- 80% two year survival
Combination Immunotherapies
Combination Therapy
Mechanisms of Action
Phase
Indication
Nivolumab + ipilimumab
Anti-PD1 + anti-CTLA-4
I/II
Gastric, TNBC, PA, SCLC,
Bladder, Ovarian
II/III
Melanoma, RCC
II
SCLC, GBM, NSCLC
Nivolumab + BMS-986016
Anti-PD1 + anti-LAG3
I
Solid tumors
Nivolumab + Viagenpumatucel-L
Anti-PD1 + vaccine
I
NSCLC
Nivolumab + urelumab
Anti-PD1 + anti-4-1ββ
I/II
Atezolizumab + MOXR0916
Anti-PDL1 + anti-OX40
I
Solid Tumors
Atezolizumab + varlilumab
Anti-PDL1 + anti-CD27
II
RCC
Atezolizumab + GDC-0919
Anti-PDL1 + IDO inhibitor
I
Solid Tumors
Epacadostat + atezolizumab,
durvalumab, or pembrolizumab
IDO inhibitor + anti-PDL1
or anti-PD1
I/II
Solid Tumors
Pembrolizumab + T-Vec
Anti-PD1 + vaccine
III
Melanoma
Durvalumab + tremelimumab
Anti-PDL1 + anti-CTLA-4
I/II
Melanoma
I/II/III
Pidilizumab + dendritic cell/RCC
fusion cell vaccine
Anti-PD1 + vaccine
Solid Tumors, B-Cell NHL
SCCHN
II
Mesothelioma, UBC,
TNBC, PA
III
NSCLC, Bladder
II
RCC
Immunotherapy Plus Chemotherapy
Combination Therapy
Mechanisms of Action
Phase
Indication
Nivolumab + platinum doublet
chemo
Anti-PD1 + chemotherapy
III
NSCLC
Pembrolizumab + cisplatin
Anti-PD1 + chemotherapy
III
Gastric
Pidilizumab + lenalidomide
Anti-PD1 + chemotherapy
I/II
Multiple Myeloma
Pidilizumab +sipuleucel-T +
cyclophosphamide
Anti-PD1 + vaccine +
chemotherapy
II
Prostate
Atezolizumab +
carboplatin/paclitaxel +/bevacizumab
anti-PDL1 + chemotherapy
+/- anti-VEGF
III
NSCLC
Immunotherapy Plus Targeted Therapy
Combination Therapy
Mechanisms of Action
Phase
Indication
Atezolizumab + bevacizumab
Anti-PDL1 + anti-VEGF
II/III
Atezolizumab + cobimetinib
Anti-PDL1 + MEK
inhibitor
I
Solid Tumors
Atezolizumab + vemurafenib
Anti-PDL1 + BRAF
inhibitor
I
Melanoma
Atezolizumab + erlotinib or
alectinib
Anti-PDL1 =EGFR or
ALK inhibitor
I
NSCLC
Nivolumab + bevacizumab
Anti-PD1 + anti-VEGF
II
RCC
Pembrolizumab + pazopanib
Anti-PD1 + tyrosine
kinase inhibitor
I
RCC
Pembrolizumab + dabrafenib
+ trametinib
Anti-PD1 + BRAF
inhibitor + MEK inhibitor
I/II
Melanoma
Durvalumab + dabrafenib +
trametinib
Anti-PDL1 + BRAF
inhibitor + MEK inhibitor
I/II
Melanoma
Nivolumab + sunitinib,
pazopanib or ipilimumab
Anti-PD1 + RTK inhibitor,
RTK inhibitor
I
RCC
RCC
Combination of PD-1, PDL-1 and CTLA-4 Blockade
 higher clinical response rates than single agent
- melanoma, NSCLC, head and neck
 lower tolerability and higher discontinuation rates
 management of toxicity in broad patient populations
in community settings
 cost
 dosing and sequence
 competition and cutting corners in dose
optimization
Cell-Based Therapies
Immunotherapeutic Strategies to Enhance Immune
Responses to Patient-Specific Tumor Neoantigens
Immune
Checkpoint
Modulation
Cancer Neoantigen
Vaccines
Adoptive Cell Therapy
TILs, TCRs, CARs
Adapted From: T. N. Schumacher and R. D. Schreiber (2015) Science 348, 69
Adoptive T Cell Transfer in Cancer Immunotherapy
• collect patient’s T cells
• expand T cells ex vivo
• +/- lymphodepletion/conditioning
prior to reinfusion of expanded cells
TILs
• no modification only
expansion
TCRs and CARs
• transfection with genes for
T cell receptors (TCRs)
or chimeric antigen
receptors (CARs) against
specific tumor neoantigens
Design of Chimeric Antigen Receptors for Cancer Immunotherapy:
Engineered Combination of Elements of Antibody
Structure and T Cell Receptors
C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
Design of Chimeric Antigen Receptors for
Cancer Immunotherapy
C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
Design of Chimeric Antigen Receptors
‘armored CARs’
 incorporation of additional T cell activation mechanisms
into CAR-T cells to counter immunosuppression in the
tumor microenvironment
‘switchable CARs’
 integration of ‘kill switches’ (reversible/irreversible) to shut
down CAR-T cells for better control of toxicities
Future Needs in the Evolution CAR Therapy
 need to establish efficacy in solid tumors
 lymphodepletion by preconditioning appears necessary
for successful treatment and CAR-cell persistence
 reduction of AEs and CRS
- CRS is observed more frequently in patients with high
tumor burden
- merits of prior Rx tumor-debulking in improving safety
profile?
 dose selection is difficult since transferred cell expansion
in vivo appears highly variable
 reduce cost and complexity of ex vivo scale up of cells
for reinfusion
 ‘off-the-shelf’ use of allogeneic cells HLA matched to
recipients
NK Cells: The Next Target for Selective Activation
of Anti-Tumor Cell Responses?
The Next Generation of Immuno-Oncology Therapeutics
Beyond CTLA-4 and PD-1/PD-L1 as
Targets for Cancer Immunotherapeutics
Next Generation Immunotherapies
 better response rates
 extended durable clinical benefits
 better tolerability
 improved knowledge of how to best use
I/O combinations or I/O plus SOC
 predictive biomarkers for reliable stratification of
responder and non-responder patients and
monitoring treatment efficacy
The Complex Dynamics of the
Host Immune System-Tumor Ecosystem
 corrupted tumor microenvironment
- protumor inflammatory responses and
immunosuppressive signals
 intrinsic immune checkpoint regulators (suppression)
- CD28-CTLA-4, PD1-PD-L1, TIM-3, LAG
 blockade of T cell infiltration
 extrinsic checkpoint regulators (suppression)
- regulatory T cells (Tregs), myeloid suppressor cells
(MDSC)
 T cell energy and exhaustion (suppression)
 immune evasion (escape)
- antigen-deletion clones, neoantigens with low affinity
Negative Immune Checkpoint Regulators (NCRs)
as New Targets for Next-Generation Immunotherapeutics
 TIM-3
- T-cell immunoglobulin and mucin-containing
protein 3
 LAG-3
- lymphocyte-activated gene-3 (CD223)
 TIGIT
- T-cell immunoreceptor with Ig and ITIM domains
 BTLA
- B- and T-lymphocyte attenuator
 VISTA
- V-domain Ig suppressor or T cell activatin
The Immunosuppressive Tumor Microenvironment
The New Frontier, A Wealth Of Targets
/ TDO
From: K.M. Mahoney et al. (2015) Clinical Therapeutics 34, 764
The Tumor Microenvironment and the
“Stromagenic Switch”
The Stromagenic Switch
 role of stroma surveillance mechanisms in preventing
tumorigenesis or imposition of dormant states
 transition of cancer-associated stromal cells (CASC) to
protumorigenic drivers
- inflammation
- ECM remodeling
- immunosuppressive signaling
- M1 to M2 macrophage conversion
- angiogenesis
- invasion, EMT and metastasis
 altered stromal elements as new Rx Targets
Predictive Identification of Responder and Non-Responder Patients
Why Are Some Cancer Types
More Responsive to Immunotherapy?
More Responsive
Less Responsive
 melanoma
 pancreatic
 NSCLC
 colorectal
 bladder
 ovarian
 renal
 head and neck
Immunogenic Versus Non-Immunogenic
Tumor Microenvironments?
Immunogenic
Non-Immunogenic
 ‘hot’
 ‘cold’
 ‘inflamed’
 ‘non-inflamed’
 ‘stimulatory’
 ‘silent’
Immunogenic Versus Non-Immunogenic
Tumor Microenvironments
Immunogenic
Non-Immunogenic
 ‘hot’
 ‘cold’
 ‘inflamed’
 ‘non-inflamed’
 ‘stimulatory’
 ‘silent’

high mutagenic
burden
 low mutagenic
high tumor
neoantigen
expression
 low tumor

burden
neoantigen
expression
Cancer Immunotherapy
 in situ infiltration of activated T cells is critical
for therapeutic response and tumor regression
 not all immune infiltrates are equal
 therapeutic success depends on the dynamics
balance of immune activation/suppression
factors in the tumor microenvironment
T-Cell Tumor Infiltration
From: K. Wkatsuki et al. (2013) Spandidos Publications (DOI: 10.3892/or.2013.2302
Profiling Intratumoral Immune Cell Populations
positive prognosis: immune activation dominant
 cytotoxic T cells and memory-T cells
 antigen-presenting cells
negative prognosis: immune suppression dominant
 T regulatory cells (Treg)
 Th2 helper T cells
 myeloid-derived suppressor cells
 M2 phenotype macrophages
The Immunophenotype
Biomarker Development for Immuno-Oncology
Developing An Immunoscore for Individual Patients
The Paucity of Biomarkers to Identify Responder
and Non-Responder Patients
 major problem in patient selection and cost of futile Rx
 conflicting data on relationship of PD-L1 expression
and responsiveness to anti-PD1 therapy
- KEYNOTE – 001: 45.2% of patients below
predetermined PD-L1 cutoff still responded to
pembrolizumab
 use of different antibody assay platforms and PD-L1
cutoff levels in different clinical trials
PD-L1 Expression and Response Rate (RR) for
Immune Checkpoint Modulation in Melanoma
Agent
Median PFS
Months
Response Rate
PD-L1
none/low
PD-L1
high
PD-L1
none/low
PD-L1
high
iplimumab
18
21
3
4
nivolumab
41
57
5
14
iplimumab
plus
nivolumab
54
72
11
14
From: E.I. Buchbinder and F.S. Hodi (2016) Nature Rev. Oncol 13, 47
Immunophenotyping:
Biomarkers for Evaluation of Immune System ‘States’ and Prediction
of Responder: Non-Responder Cohorts for Immunotherapy
 characterization of immune functions in three
anatomic compartments
- lymphoid organs/nodes, systemic circulation
and neoplastic lesions
 formation of international consortium to
establish a classification metric designated
TNM-I (TNM-Immune)
Profiling of Intratumoral Core (GZMB) Cytotoxic T Cells and
Lymphatic Vessel Density at the Invasive Margin (PDPN)
in 838 CRC Patients and Relationship to Overall Survival
No
Metastasis
Metastasis
No
Metastasis
Metastasis
From: B. Mlecnik et al. (2016) Science Translational Medicine 8, 327ra26
The Tumor Mutational Landscape and
Responses to Immunotherapy Agents
 hypothesis that high(er) non-synonymous mutation
burden generates neoantigens recognized by the
immune system
 patients with higher neoantigen burden exhibit
higher durable clinical benefit (DCB)
 ‘mutanome’ profiling
- ID mutant nonamer peptides with <500nM binding
affinity for patient-specific class I HLA alleles
 combination with targeted anti-cancer agents
- increase neoantigen release?
Estimates of Likelihood of Neoantigen Expression Based
on Somatic Mutation Prevalence in Different Tumor Types
Adapted from: T. N. Schumacher and R. D. Schreiber (2015) Science 348, 69
and L. B. Alexandrov et al. (2013) Nature 500, 415
The Tumor Mutational Landscape and Response
to Immunotherapy Agents
 higher non-synonymous mutation burden correlates with
improved objective response, PFS and durable clinical
benefit
 highest response rates in melanoma and NSCLC
- chronic mutagen burden (UV, tobacco carcinogens)
 high inter-patient variation in NSCLC
- smokers vs non-smokers
- paradoxical greater DCB in smokers to PD-1 blockade
Molecular ‘Smoking Signature’ in NSCLC and
PFS in Patients Treated with Pembrolizumab
(smokers)
(non-smokers)
From: N.A. Rizvi et al. (2016) Science 348, 124
Wagner Parsimony Profiling of Intratumoral Clonal Heterogeneity in 11
Lung Adenocarcinomas and Different Trunk (Blue), Branch (Green)
and Private (Red) Branches
From: J. Zhang et al. (2014) Science 346, 256
Neoantigen Clonal Architecture and Clinical
Benefit of Immune Checkpoint Blockade
(anti-PD1 pembrolizumab)
From: N. McGranahan et al. (2016) Science DOI.10.1126/aaf490
Use of Combination Therapies to Increase
Neoantigen Expression and Release
Lessons from Breast Cancer Trials of HER-2 Kinase Inhibitors
 trastuzumab as a singular success story for HER-2
positive breast cancer
 exploration of value of small molecular TKls
- lapatinib (EGFR + HER2) afatinib (EGFR, HER2,
HER4) neratinib (HER1, HER2, HER4)
- inferior outcomes and higher toxicity
 is consistent superiority of trastuzumab over other
TKIs due to additional effects on immune responses?
- tumors enriched for immune signatures benefit from
trastuzumab
- level of tumor-infiltrating lymphocytes predicts
trastuzumab benefit
- not all studies concordant
Potential Previously Unrecognized Immunostimulatory
Effects of Conventional Chemotherapeutics?
 low dose, metronomic administration schedule with
immune checkpoint agents and enhanced
responses?
 off-target effects in activation of immune system
directly?
- 5-fluorouracil killing of tumor-associated myeloid
suppressor cells
 value in increasing mutagen burden and neoantigen
expression as activation trigger for immune
response?
Oncolytic Pipeline
Biocentury 02/29/16
Science (2014) 345, 1254, 665
Immunogenetics:
Individual Genetic Variation in Immune Responses
 how does individual genetic variation affect the
nature and intensity of T cell responses?
 identification of single nucleotide polymorphisms
that influence susceptibility/relative resistance to
autoimmune diseases and responses to pathogens
 wide individual variation and eQTLs polymorphisms
for activation-induced cytokine levels
 no information on how these parameters may link
to individual variation in immunotherapy-induced
anti-tumor responses
Imaging Endpoints for
Immunotherapy Response Evaluation
 limitations of traditional RECIST criteria due
to ‘pseudo-progression’ caused by T cell
infiltration/inflammation edema
- tumor size and density
 nivolumab CheckMate 057 trial
- reported short PFS but significant
prolongation of OS
- superiority versus docetaxal at 9 months
 development of irRECIST criteria
Need for New Minimally-Invasive Assays for
Monitoring Patient Responses to Immunotherapy
 ‘static’ snapshot of immune profile in resected
lesions/biopsies versus longitudinal monitoring of
dynamic changes with tumor progression /Rx responses
 how far does the immune profile assayed in blood
(liquid biopsy) mirror intratumoral events in anatomically
dispersed metastases?
- immune cell subsets?
- cytokines?
- ctDNA?
- exosomes?
Does the Gastrointestinal Microbiome Affect
Immunotherapy Efficacy?
A Role for the Microbiome in Regulating Systemic Cancer
Risk, Immune Responses and Responses to Therapy?
 gut microbiota dramatically impacted by many anti-
neoplastic drugs
 translocation of gut microbiota across intestinal
epithelium and activation of DCs in lymphodepleting irradiation and improved responses to
ACT
 Bifidobacterium prevalence influences efficacy of
anti-PD-1 and anti-CTLA-4 mAb therapy and efficacy
reduced by antibiotic therapy
Immune-Medicated Colitis in Melanoma Patients Treated with
CTLA-4 Blockade Correlates with Lower Levels of
Bacteroides Phylum Families in the Gut Microbiome
Bacteroidaceae
Adundance
Rikenellaceae
Taxonomic Units
Bernesiellaceae
Adapted from: K. Dubin et al. (2016) Nature Communications 10391
Could Selective Manipulation of Gut Microbiota Impact
Cancer Risks and/or Improve Efficacy
of Some Anti-Cancer Therapies?*
 adverse impact of antibodies in eliminating ‘beneficial’ species?
 use of antibiotics to reduce untoward bacterial species?
 use of probiotics to optimize ‘beneficial’ species?
 postbiotics: metabolic products from ‘beneficial’ species that
exert therapeutically valuable effects?
*L. Zitvogel et al. (2015) Sci. Trans. Med 7, 2741psl
Cancer and the gut microbioata. an unexpected link
Price
and
Affordability!!!
The Cost of Complex Cancer Care
• AML
• An 18 month journey to
remission
• 3 approved drugs, 2
investigational drugs
• 2 stem cell transplants
• $4 million dollars
Evan Johnson sits on a terrace at the Mayo Clinic Hospital, Methodist Campus
in Rochester, Minn. during the summer of 2014.
From: Winslow, R. (2016) Cancer Treatment's New Direction. WSJ
Is Widespread Adoption of Immunotherapy
Economically Feasible?
 direct Rx cost
 indirect care cost
 escalating cost of combination
regimens (> $200K)
 extravagant cost of cell-based
therapies ($500K - $1.5 million)
 complex clinical management
challenges and compatibility with
community oncology services
April 2016
What Are We Willing to Pay for Added Months of
Survival in Cancer?
Lifetime cost above
standard care
If cancer is on par with other
diseases ($150,000 per life year
gained), months of added overall
survival benefit needed
Treating cancer as worthy of
much higher reimbursement
($250,000 per life year gained),
months of added overall
survival benefit needed
$50,000
4 months
2.4 months
$100,000
8 months
4.8 months
$150,000
12 months
7.2 months
$200,000
16 months
9.6 months
$250,000
20 months
12 months
$300,000
24 months
14.4 months
$350,000
28 months
16.8 months
$400,000
32 months
19.2 months
$450,000
36 months
21.6 months
$500,000
40 months
24 months
Source: Pink Sheet 13 Sept. 2010. Adapted from S. Ramsey FHCRC, ASCO 2010
Performance Comparison for New Anti-Cancer Drugs Approved
2002-2014 for Top Ten Pharmaceutical Companies
Gains in Progression-Free Survival (PFS) and Overall Survival (OS) for 71 Drugs Approved by the FDA
From 2002 to 2014 for Metastatic and/or Advanced and/or Refractory Solid Tumors
PFS
median
2.5 months
OS
median
2.1 months
From: T. Fojo et al. (2014) JAMA Otolaryngology–Head & Neck Surgery 140, 1225
Value-Based Rx Pricing of Oncology Therapeutics
 outcomes-based payments
 indication-specific pricing
 reference pricing (maximum price for all drugs in a
therapeutic class)
Deconvolution of the Multi-Dimensional Matrix of
Immuno-Oncology Therapeutics
•
•
•
•
clonal
heterogeneity
mutagen burden
neoantigen profile
tumor
host
immune
response
tumor
microenvironment
• complex non-immune cell
contributions to suppressive
environment
• localization of immune cells/
soluble mediators and impact of Rx
balance of
stimulatory and
suppressive
factors
The Evolution of Cancer Immunotherapeutics
 likely to become SOC in increasing number of indications
 need for better informed rationale for combination regimens
 identification of new I/O intervention points
- Tregs, MDSC, NK cells, TME resistance mechanisms
 risk of MDR and recurrence in long DOR patients?
 improved immunophenotyping (immunoscore) of individual
patients for predictive ID of responders and non-responders
 intense competitive corporate landscape and massive
financial investments
 price and new pharmaco-economic-realities for approval and
reimbursement